Development of a Lightning NOx Algorithm for WRF-Chem

1. Development of a Lightning NOx Algorithm for WRF-Chem Amanda Hopkins Hansen Department of Meteorology Florida State University ahopkins@met.fsu.edu Henry E. Fuelberg Florida State University Kenneth E. Pickering NASA Goddard Space Flight Center

2. O2 N2 N2 N2 N2 O2 N2 N2 O2 N2 N2 N2 O2 N2 + O2 NO + NO Air is ~78%N2 and ~21% O2

3. Oxidation of methane Oxidation of carbon monoxide Ozone Formation in the Troposphere N2 +O2  NO + NO NO + O3  NO2 + O2 NO2 + hνNO + O O + O2 + M  O3 + M Air is ~78%N2 and ~21% O2 hn = 290nm <l <410nm • These reactions set up the photostationary state. • O3 depends on the rates of formation and photolysis of NO2 • In order for ozone to increase, NO2 formation • must happen without destroying O3

4. Rationale - why is LNOx important June, July, and August 1. Lightning is an important source of NOx in the relatively clean upper troposphere December, January, and February Pressure (hPa) Zonal Mean Lightning NOx production (10-2kg/s) From the NASA GISS GCM

5. Rationale - why is LNOx important • 2. LNOx indirectly affects our local air quality and global climate • Has a strong influence on Ozone (O3) and hydroxyl • radical (OH) concentration NOx [NO+NO2]: • is a primary pollutant found in photochemical smog • is a precursor for tropospheric ozone formation TROPOSPHERIC OZONE: • is the third most important greenhouse • gas • impacts the Earth’s radiation budget and • can cause changes in atmospheric • circulation patterns. • is toxic to humans, plants and animals. Photo courtesy of University of California at Berkley

6. Global NOx Budget 3. LNOx is difficult to realistically model, but is Important in the global budget of NOx Figures from Global Emissions Inventory Activity data sets Note: lightning figures are calculated by model internally

7. LNOx algorithm for WRF-Chem with Parameterized Convection what are the necessary ingredients? • Flash rate parameterization • 2) Average NO production per flash • 3) Method of specifying the vertical distribution • of LNOx emissions

8. Schemes often used for Flash Rate Production 1. Cloud Top Height [Price and Rind, 1992; Michalon et al., 1999] 2. Convective Precipitation Rate [Meijer et al., 2001; Allen and Pickering, 2002] Separate parameterizations are needed to simulate land and oceanic lightning 3. Upward Cloud Mass Flux/Updraft Velocity [Grewe et al., 2001; Allen and Pickering, 2002] • This scheme cannot be developed based on observations. Model output must be used. Therefore, the relationship between these variables and flashrate will be model-dependent. None of these schemes yield global flash distributions that compare well with satellite flash observations. What is needed is a more microphysically-based scheme. Possibilities include the schemes by Deierling et al. (2005) and Futyan and Del Genio (2007).

9. Flux Hypothesis Deierling et al., 2005 They propose that lightning frequency, f, is proportional to the product of the precipitation rate, p, and the mass upflux of ice crystals, Fi­. The charging mechanism used in the this study involves rebounding collisions between heavy graupel pellets and the lighter ice crystals (Reynolds et al., 1957; Takahashi, 1978; Jayaratne et al., 1983; Baker et al, 1987). This process creates a vertical electric dipole by gravitational separation of oppositely charged graupel and ice crystals. This scheme would work very well on the cloud-scale We need a Regional scale relationship!!!

10. Convective Radar Storm Height Futyan and Del Genio, 2007 Cloud top height may be several kilometers higher than the height of significant radar signal fifth order power law for radar top height (above surface) second order for radar top height above 0 degree isotherm.

11. WSR-88 Doppler Radar and the Lightning Detection and Ranging (LDAR) data Futyan and Del Genio (2007) used TRMM data for their research: we will use Doppler radar and LDAR data to form a relationship between flash rate and radar storm height. 3-D Lightning Mapping locations to be incorporated Kennedy Space Center, Florida** - LDAR/Vaisala Huntsville, Alabama – NM Tech Dallas, Texas – LDAR/Vaisala Washington D.C. – NM Tech **We will start with KSC

12. WSR-88 Doppler Radar and the Lightning Detection and Ranging (LDAR ) data from Kennedy Space Center (KSC) Doppler Radar Melbourne, Florida KSC LDAR. Sensor 0 is the central LDAR receiver. (After Poehler and Lennon 1979 and Vollmer 2002)

13. WSR-88 Doppler Radar and the Lightning Detection and Ranging (LDAR ) data from Kennedy Space Center (KSC) The location of the Melbourne, Florida National Weather Service Office WSR-88D radar in relation to the Kennedy Space Center. Range rings are provided at 10 km intervals. (After Nelson 2002).

14. WDSS: Warning Decision Support System This will provide a way to visualize lightning flashes with radar reflectivity The image depicts individual Lightning Detection and Ranging (LDAR) sparks as measured by the Kennedy Space Center LDAR network, the cloud-to ground strikes measured by the National Lightning Detection Network (NLDN), and quality controlled reflectivity data from the Melbourne NWS radar. From the LDAR sparks we can calculate flash rate.

15. WDSS: Warning Decision Support System Cross section view of cell 49 in previous slide

16. LNOx algorithm for WRF-Chem what are the necessary ingredients? Lightning Flash Rate Parameterization • Flash rate (F) will be calculated based on the following relationship: • F=AHn • Relationship between Radar storm height above the freezing level • and LDAR data is needed to determine A and n above. • Radar Reflectivity is calculated within WRFChem using hydrometeors • from microphysical scheme (WSM6) coupled with the Kain-Fritsch • cumulus parameterization. • Convective storm height (H) is found using the 20dbz contour and the • freezing level is obtained from the WRF temperature field.

17. LNOx algorithm for WRF-Chem what are the necessary ingredients? LNOx parameterization • Production rate of NO from both IC and CG lightning: • -500 moles per flash (Ott et al.,in progress) • Vertical Distribution of NO: • Pickering et al., 1998: -Wind fields from Goddard Cumulus • Ensemble (GCE) model were used to redistribute LNOx throughout • the duration of the storm. Profiles were constructed for mid-latitude • continental, tropical continental, and tropical marine regimes • based on profiles computed for individual storms in each regime

18. Verification Simulations for Summer 2004 INTEX-NA period over eastern US and comparisons with aircraft data DC-8 flight track showing the path through the Huntsville, AL storms We have the same data for Kennedy Space Center Plot courtesy of Mike Porter

19. Summary • Flash rate parameterization being developed for WRF-Chem usingobserved radar and 3-D lightning mapping array data. • WRF-Chem will first be tested with the Futyan and Del Genio (2007) relationship and then with the newly developed scheme. • Existing NO production per flash and vertical distribution information will be used. • Testing of WRF-Chem with lightning will be conducted using aircraft NOx observations from the ICARTT (NASA and NOAA data) experiment from Summer 2004.